An RVFLNs ensemble modeling method integrating PCA and PSO: Application to yield prediction of Nongxiang Baijiu

Abstract

To investigate the mapping relationship between key process parameters and Baijiu yield during the steaming and distillation process of Baijiu fermented material (SDP-BFM) and to optimize these parameters for enhanced production efficiency, a Random Vector Functional Link Networks (RVFLNs) ensemble modeling method integrating Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) is proposed for yield prediction of Nongxiang Baijiu. First, to improve computational efficiency and avoid multicollinearity, PCA is applied to reduce the dimensionality of the high-dimensional output matrix of RVFLNs, following data cleaning and feature selection. Second, a PSO algorithm is introduced to optimize both the number of hidden layer nodes in sub-learners and the weight combination strategy of the ensemble linear regression method, ultimately achieving Baijiu yield prediction modeling based on the PSO-P-ERVFLNs algorithm. Comparative experiments demonstrate that the optimization strategy introduced by PSO can enhance the prediction accuracy of the RVFLNs algorithm and alleviate overfitting. Moreover, the proposed algorithm exhibits better computational efficiency and higher estimation accuracy, enabling accurate prediction of Baijiu yield during the SDP-BFM.